When a human is exposed to practically any form of stimulus, the human brain reacts in a physical manner. A variety of techniques have been proposed for decoding these reactions, with many of the techniques dependent on the form of the neural data encoded. For decoding of human neural activity, functional MRI (fMRI) analysis of BOLD (Blood oxygen level dependent) activations has resulted in the ability to assess stimulus evoked activity on a fine (millimeter) spatial scale. The density of information in this approach has resulted in a wealth of analytical studies and attempts at neural decoding.
One of the first attempts at statistically sophisticated fMRI decoding of neural activity in response to visual stimuli, known as multivariate pattern analysis (MVPA), relied on the fact that different stimuli will induce neural responses with spatially distinct patterns of activity and that classifier and machine learning algorithms can operate to identify optimal decision boundaries for appropriate classification of stimulus categories. Later studies transformed sets of stimulus images and fMRI activity into a common vector space where fMRI activity can not only be used to identify which stimulus images have been presented, but can reconstruct those images based on putative features encoded by particular voxels. Unfortunately, such techniques are highly computationally intensive.